"""simple docstring"""



import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
   import torch






@require_torch
@require_optimum
@slow
class  snake_case__      (       unittest.TestCase       ):


 def 						a__   ( self						):
        __a       =    "hf-internal-testing/tiny-random-t5"
        __a       =    AutoTokenizer.from_pretrained(lowerCamelCase						)
        __a       =    AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase						)

        __a       =    tokenizer("This is me"    ,		return_tensors="pt"						)

        __a       =    model.to_bettertransformer()

        self.assertTrue(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()						)						)

        __a       =    model.generate(**lowerCamelCase						)

        __a       =    model.reverse_bettertransformer()

        self.assertFalse(any("BetterTransformer" in mod.__class__.__name__ for _, mod in model.named_modules()						)						)

        with tempfile.TemporaryDirectory() as tmpdirname:
               model.save_pretrained(lowerCamelCase						)

               __a       =    AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase						)

               self.assertFalse(
                   any("BetterTransformer" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()						)						)

               __a       =    model_reloaded.generate(**lowerCamelCase						)
               self.assertTrue(torch.allclose(lowerCamelCase    ,		lowerCamelCase						)						)






 def 						a__   ( self						):
        __a       =    "hf-internal-testing/tiny-random-t5"
        __a       =    AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase						)

        __a       =    model.to_bettertransformer()

        with tempfile.TemporaryDirectory() as tmpdirname:
               with self.assertRaises(lowerCamelCase						):
                      model.save_pretrained(lowerCamelCase						)

               __a       =    model.reverse_bettertransformer()
               model.save_pretrained(lowerCamelCase						)

"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__:Optional[Any]									=			{"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE__:Optional[Any]									=			["""ReformerTokenizer"""]

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE__:List[str]									=			["""ReformerTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE__:int									=			[
       """REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """ReformerAttention""",
       """ReformerForMaskedLM""",
       """ReformerForQuestionAnswering""",
       """ReformerForSequenceClassification""",
       """ReformerLayer""",
       """ReformerModel""",
       """ReformerModelWithLMHead""",
       """ReformerPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer import ReformerTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer_fast import ReformerTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_reformer import (
          REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          ReformerAttention,
          ReformerForMaskedLM,
          ReformerForQuestionAnswering,
          ReformerForSequenceClassification,
          ReformerLayer,
          ReformerModel,
          ReformerModelWithLMHead,
          ReformerPreTrainedModel,
      )

else:
   import sys

   SCREAMING_SNAKE_CASE__:int									=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
